Data Lake VS Data warehouse ... 3 reasons to adopt the best data structure
What are differences between the DataLake and the DataWarehouse?
Data can be stored in several ways. Among them are two of the best-known ways: Data Lake and Data Warehouse. Both of these technologies provide companies with a large data storage capacity.
However, despite their common function, Data Lake and Data Warehouse have distinctly unique features. Today, many companies still find themselves making the wrong choice. This choice can cause them to lose data and time in their storage process. For this reason, it is essential to know the difference between Data Lake and Data Warehouse.
What is the difference between them? Their data structure
Before choosing one of these storage methods, it is essential for a company to ask itself to what extent it needs to structure its data. This audit phase must be completed after the choice of technology in order to guarantee its future use. Becoming aware of the criteria of these needs will allow the organization to take a closer look at the advantages and disadvantages of the proposed storage technologies and thus avoid losing time or data during the storage process.
Data formats
Several companies need to store a large volume of data from different sources and in different formats (such as mp4, mp3, png, jpeg, svg, etc.).
Therefore, the flexibility of the Data Lake allows companies to have a database that keeps data in its native format, allowing data scientists to have flexibility in data analysis and model development.
Data accuracy and veracity
On the other hand, many companies want the ability to keep their margin of error under tight control. They need to have data with a strong structure, in order to have access to accurate and precise data.
For this purpose, the organized data warehouse architecture makes it possible to list and centralize company data from the same source, in order to guarantee its reliability and make it more accessible to less technical users.
Data harmonization
Some companies are faced with a complex need, which can be engendered by the inheritance of multiple pieces of data, following the acquisition of a subsidiary, for example. In this case, some companies want to be able to filter and structure their data.
In addition, they want to be able to store data from different sources and formats. In such cases, it is possible to opt for both, Data Lake and Data Warehouse, in order to benefit from the different advantages of the storage solutions.
Furthermore, if a company chooses to use both database technologies, it is essential to identify in advance which technology will be the primary one to ensure proper data storage and structure.
To conclude, both technologies support the storage of a significant volume of data. They are also categorized according to their advantages, which are the flexibility of the DataLake and the organized DataWarehouse architecture. Despite their differences, these two technologies are compatible with each other and can be used by the same company.
Today, Nodata being technology agnostic, adapts perfectly to both storage solutions.
To this end, Nodata's teams help and accompany companies' technical contacts to structure their needs through workshops, so that they can make a choice that is adapted to their needs.